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Abstract
There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the Gene-Lifestyle Interactions Working Group within the Cohorts for Heart and Aging Research in Genetic Epidemiology Consortium has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identify and confirm 5 loci (6 independent signals) interacted with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrate that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated heritability is significant (P < 0.02) for low-density lipoprotein cholesterol and triglycerides in Cross-Population data. Our study improves the understanding of the genetic architecture and environmental contributions to complex traits.
Here, the authors report 5 loci interacting with smoking/alcohol for serum lipids using a new method akin to Mendelian randomization. They unveil significant heritability through gene-environment interaction and mediation, enhancing understanding of complex trait genetics.
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1 Case Western Reserve University, Department of Population and Quantitative Health Sciences, School of Medicine, Cleveland, USA (GRID:grid.67105.35) (ISNI:0000 0001 2164 3847)
2 National Institutes of Health, Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, USA (GRID:grid.94365.3d) (ISNI:0000 0001 2297 5165)
3 The University of Texas Health Science Center at Houston, Human Genetics Center, Department of Epidemiology, School of Public Health, Houston, USA (GRID:grid.267308.8) (ISNI:0000 0000 9206 2401)
4 University of Southern California, Division of Biostatistics, Department of Population and Public Health Sciences, Los Angeles, USA (GRID:grid.42505.36) (ISNI:0000 0001 2156 6853)
5 Washington University School of Medicine, Center for Biostatistics and Data Science, Institute for Informatics, Data Science and Biostatistics, St. Louis, USA (GRID:grid.42505.36)
6 Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, France (GRID:grid.42505.36); Harvard T.H. Chan School of Public Health, Program in Genetic Epidemiology and Statistical Genetics, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X)